Primary Analysis (MAR)
Hypothesis 1 - Affect measures predict depression retrospectively
m1 <- with(imp_MAR,
lm(BDI_prae ~ scale(mean_neg_T5) + scale(mean_pos_T5) +
scale(rmssd_neg_T5) + scale(rmssd_pos_T5)))
nice_table(summary(pool(m1), conf.int = TRUE))
Term | estimate | std.error | statistic | df | p | 2.5 % | 97.5 % | 95% CI |
|---|---|---|---|---|---|---|---|---|
(Intercept) | 22.69 | 0.88 | 25.82 | 114.64 | < .001*** | 20.95 | 24.43 | [20.95, 24.43] |
scale(mean_neg_T5) | 2.23 | 1.24 | 1.80 | 111.54 | .075 | -0.23 | 4.68 | [-0.23, 4.68] |
scale(mean_pos_T5) | 2.36 | 1.45 | 1.63 | 108.43 | .107 | -0.51 | 5.23 | [-0.51, 5.23] |
scale(rmssd_neg_T5) | -0.58 | 1.11 | -0.52 | 115.67 | .603 | -2.77 | 1.62 | [-2.77, 1.62] |
scale(rmssd_pos_T5) | -0.41 | 1.29 | -0.32 | 114.14 | .749 | -2.97 | 2.14 | [-2.97, 2.14] |
# R-Squared
mean(sapply(m1$analyses, function(m) summary(m)$r.squared))
## [1] 0.03687233
Hypothesis 2 - Affect measures predict depression prospectively
m2 <- with(imp_MAR,
lm(BDI_post ~ scale(mean_neg_T55) + scale(mean_pos_T55) +
scale(rmssd_neg_T55) + scale(rmssd_pos_T55)))
nice_table(summary(pool(m2), conf.int = TRUE))
Term | estimate | std.error | statistic | df | p | 2.5 % | 97.5 % | 95% CI |
|---|---|---|---|---|---|---|---|---|
(Intercept) | 11.88 | 1.04 | 11.45 | 74.60 | < .001*** | 9.82 | 13.95 | [9.82, 13.95] |
scale(mean_neg_T55) | -0.84 | 1.51 | -0.56 | 92.32 | .578 | -3.83 | 2.15 | [-3.83, 2.15] |
scale(mean_pos_T55) | -1.50 | 1.84 | -0.82 | 79.88 | .417 | -5.17 | 2.17 | [-5.17, 2.17] |
scale(rmssd_neg_T55) | 1.52 | 1.17 | 1.30 | 82.02 | .199 | -0.81 | 3.85 | [-0.81, 3.85] |
scale(rmssd_pos_T55) | -0.60 | 1.53 | -0.39 | 69.29 | .697 | -3.64 | 2.45 | [-3.64, 2.45] |
# R-Squared
mean(sapply(m2$analyses, function(m) summary(m)$r.squared))
## [1] 0.05049228
Hypothesis 3 - Affect measures change over time
Mean Positive Affect
t.test(df_final$mean_pos_T5, df_final$mean_pos_T55, paired = TRUE)
##
## Paired t-test
##
## data: df_final$mean_pos_T5 and df_final$mean_pos_T55
## t = -1.3065, df = 128, p-value = 0.1937
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.008052211 0.001647616
## sample estimates:
## mean difference
## -0.003202297
cohen.d(df_final$mean_pos_T5, df_final$mean_pos_T55, paired = TRUE)
##
## Cohen's d
##
## d estimate: -0.1160853 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.29165124 0.05948068
Mean Negative Affect
t.test(df_final$mean_neg_T5, df_final$mean_neg_T55, paired = TRUE)
##
## Paired t-test
##
## data: df_final$mean_neg_T5 and df_final$mean_neg_T55
## t = 0.87931, df = 128, p-value = 0.3809
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.003986877 0.010364555
## sample estimates:
## mean difference
## 0.003188839
cohen.d(df_final$mean_neg_T5, df_final$mean_neg_T55, paired = TRUE)
##
## Cohen's d
##
## d estimate: 0.07577303 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.09416962 0.24571568
Variation Positive Affect
t.test(df_final$rmssd_pos_T5, df_final$rmssd_pos_T55, paired = TRUE)
##
## Paired t-test
##
## data: df_final$rmssd_pos_T5 and df_final$rmssd_pos_T55
## t = -0.39184, df = 128, p-value = 0.6958
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.0002449449 0.0001639675
## sample estimates:
## mean difference
## -4.048869e-05
cohen.d(df_final$rmssd_pos_T5, df_final$rmssd_pos_T55, paired = TRUE)
##
## Cohen's d
##
## d estimate: -0.04176154 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.2517352 0.1682121
Variation Negative Affect
t.test(df_final$rmssd_neg_T5, df_final$rmssd_neg_T55, paired = TRUE)
##
## Paired t-test
##
## data: df_final$rmssd_neg_T5 and df_final$rmssd_neg_T55
## t = 0.51839, df = 128, p-value = 0.6051
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.0001444791 0.0002470576
## sample estimates:
## mean difference
## 5.128924e-05
cohen.d(df_final$rmssd_neg_T5, df_final$rmssd_neg_T55, paired = TRUE)
##
## Cohen's d
##
## d estimate: 0.05608721 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.1571456 0.2693200
Hypothesis 4 - Change in affect measures predics change in depression
m4 <- with(imp_MAR,
lm(delta_BDI ~ scale(delta_mean_neg) + scale(delta_mean_pos) +
scale(delta_rmssd_neg) + scale(delta_rmssd_pos)))
nice_table(summary(pool(m4), conf.int = TRUE))
Term | estimate | std.error | statistic | df | p | 2.5 % | 97.5 % | 95% CI |
|---|---|---|---|---|---|---|---|---|
(Intercept) | -10.81 | 1.23 | -8.79 | 85.17 | < .001*** | -13.25 | -8.36 | [-13.25, -8.36] |
scale(delta_mean_neg) | 2.70 | 1.84 | 1.47 | 90.78 | .145 | -0.95 | 6.35 | [-0.95, 6.35] |
scale(delta_mean_pos) | 1.03 | 2.01 | 0.51 | 90.96 | .611 | -2.97 | 5.02 | [-2.97, 5.02] |
scale(delta_rmssd_neg) | -0.95 | 1.40 | -0.68 | 88.00 | .500 | -3.73 | 1.83 | [-3.73, 1.83] |
scale(delta_rmssd_pos) | 0.80 | 1.61 | 0.50 | 81.41 | .622 | -2.41 | 4.01 | [-2.41, 4.01] |
# R-Squared
mean(sapply(m4$analyses, function(m) summary(m)$r.squared))
## [1] 0.03700676